Business address quality: when is validation enough, and when should you assess AI?

The address exists, but is that where the delivery should go

A customer has an office and a warehouse. The order holds the full office address, and the notes say „deliveries are received by the warehouse”. The street, number and town are correct, and the problem is how they are used in this case. An address validation service will accept the address as valid.

A useful hint shows the mismatch together with the customer's note. An employee confirms the delivery location with the account manager or directly with the customer. If the warehouse address is not in the available materials, a question remains, with no guessing. A different name for a place does not always mean an error, because a customer may rightly use several locations, so the flag concerns a specific order.

How much a simpler change gives, and how much training adds

In this count rules and an address service give most of the effect for a fraction of the cost. Training makes sense at a larger scale or when the remaining errors are expensive. The 15 minutes per correction include a call to the customer and a change to the delivery, because an address error usually shows only after dispatch.

The set of training examples has to be maintained: when the process, the naming or the base model changes, the evaluation is repeated on the same test set. We include that cost in the quote together with the training itself.

What ordinary validation catches

A rule in the form will flag an empty field, forbidden characters or the wrong format. A ready-made address service is another option: Google lists Poland and the United States in the coverage of Address Validation and notes that data quality differs between countries. A model is worth testing on free-text notes, such as „goods received by the branch” or „office address for correspondence only”. Rolling out an approved change of location across systems is a separate scope of work.

Where to start

The team shows difficult addresses and correct exceptions, explains abbreviations and names the person who confirms the delivery location. We run the comparison on entries not used for adapting the model, counting missed ambiguities and unnecessary flags. We adapt models as part of private LLMs, and we build the app where an employee sees the result next to the source as part of AI apps. For the free analysis, a few examples of an error that keeps coming back and a note on who corrects it today are enough.